Assessment of heterogeneous catalysts obtained from chicken egg shells and diatomite for biodiesel production
Bibliographic record
Abstract
The high global dependence on nonrenewable energies has prompted scientific studies aimed at developing new sources of renewable energy based on biofuel production. The biodiesel used in diesel engines has been one of the viable alternatives to mitigate the problems resulting from the use of fossil fuels. The industrial production of this biofuel uses basic homogeneous catalysts, but problems related to the high cost of purifying the product and waste generation, among others, are common. These complications have led to the development of a heterogeneous catalyst, which has proved to be a good option for biodiesel production. In this respect, the aim of the present study was to formulate and assess heterogeneous catalysts derived from chicken egg shells and diatomite for biodiesel production. Initially, the egg shell was calcined, forming CaO, followed by wet impregnation with diatomite via calcination at 800 °C for 240 min, producing the catalyst DiaCaO. The catalysts were characterized by XRD, FTIR, SEM, BET and XRF analyses, which confirmed the formation of catalytic solids based on the composition and chemical structure. In relation to biodiesel production, the CaO and DiaCaO catalysts exhibited similar yields, with the CaO precursor converting around 99% into biodiesel under favorable conditions (6:1 methanol:oil molar ratio, 2% catalyst and 2 h reaction time).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".